Delete utilities.py

utilities.py

@@ -1,53 +0,0 @@
-
-import matplotlib.pyplot as plt
-import torch
-
-class Utilities:
-    def __init__(self, tokenizer, model):
-        self.tokenizer = tokenizer
-        self.model = model
-
-    def sanity_check(self, sentence, block_size):
-        # Encode the sentence using the tokenizer
-        wordids = self.tokenizer.encode(sentence)
-
-        # Prepare the padded input for the model
-        padded_sentence = wordids[:block_size] + [0] * (block_size - len(wordids))
-        input_tensor = torch.tensor(padded_sentence, dtype=torch.long).unsqueeze(0)
-
-        # Display input tensor shape
-        print("Input tensor shape:", input_tensor.shape)
-
-        # Process the input tensor through the encoder model
-        _, attn_maps = self.model(input_tensor)
-
-        # Display the number of attention maps
-        print("Number of attention maps:", len(attn_maps))
-
-        # Visualize and save the attention maps
-        for j, attn_map in enumerate(attn_maps):
-            att_map = attn_map.squeeze(0).detach().cpu().numpy()  # Remove batch dimension and convert to NumPy array
-            
-            print("map shape", att_map.shape, att_map.ndim)
-
-            # Check if the attention probabilities sum to 1 over rows
-            total_prob_over_rows = torch.sum(attn_map[0], dim=1)
-            if torch.any(total_prob_over_rows < 0.99) or torch.any(total_prob_over_rows > 1.01):
-                print("Failed normalization test: probabilities do not sum to 1.0 over rows")
-                print("Total probability over rows:", total_prob_over_rows.numpy())
-
-            # Create a heatmap of the attention map
-            fig, ax = plt.subplots()
-            cax = ax.imshow(att_map, cmap='hot', interpolation='nearest')
-            ax.xaxis.tick_top()  
-            fig.colorbar(cax, ax=ax)  
-            plt.title(f"Attention Map {j + 1}")
-            
-            # Save the plot
-            plt.savefig(f"attention_map_{j + 1}.png")
-            
-            # Show the plot
-            # plt.show()
-            
-
-